Paraflow: fast calorimeter simulations parameterized in upstream material configurations
Abstract We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector’s configuration. This may open new opportunities in high-energy physics measurements, for exampl...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-08-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-025-14604-0 |
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| author | Johannes Erdmann Jonas Kann Florian Mausolf Peter Wissmann |
| author_facet | Johannes Erdmann Jonas Kann Florian Mausolf Peter Wissmann |
| author_sort | Johannes Erdmann |
| collection | DOAJ |
| description | Abstract We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector’s configuration. This may open new opportunities in high-energy physics measurements, for example in the assessment of systematic uncertainties that are related to the detector geometry, in the inference of properties of the detector configuration, or in the automated design of experiments. As a concrete example, we parameterize normalizing-flow-based simulations in configurations of the material upstream of a toy calorimeter. We call this model ParaFlow, which is trained to interpolate between different material budgets and positions, as simulated with Geant4. We study ParaFlow’s performance in terms of photon shower shapes that are directly influenced by the properties of the upstream material, in which photons can convert to an electron-positron pair. In general, we find that ParaFlow is able to reproduce the dependence of the shower shapes on the material properties at the few-percent level with larger differences only in the tails of the distributions. |
| format | Article |
| id | doaj-art-e454d91671d04f9f82e543d672dcc2fa |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-e454d91671d04f9f82e543d672dcc2fa2025-08-20T03:46:19ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522025-08-0185811210.1140/epjc/s10052-025-14604-0Paraflow: fast calorimeter simulations parameterized in upstream material configurationsJohannes Erdmann0Jonas Kann1Florian Mausolf2Peter Wissmann3III. Physikalisches Institut A, RWTH Aachen UniversityIII. Physikalisches Institut A, RWTH Aachen UniversityIII. Physikalisches Institut A, RWTH Aachen UniversityIII. Physikalisches Institut A, RWTH Aachen UniversityAbstract We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector’s configuration. This may open new opportunities in high-energy physics measurements, for example in the assessment of systematic uncertainties that are related to the detector geometry, in the inference of properties of the detector configuration, or in the automated design of experiments. As a concrete example, we parameterize normalizing-flow-based simulations in configurations of the material upstream of a toy calorimeter. We call this model ParaFlow, which is trained to interpolate between different material budgets and positions, as simulated with Geant4. We study ParaFlow’s performance in terms of photon shower shapes that are directly influenced by the properties of the upstream material, in which photons can convert to an electron-positron pair. In general, we find that ParaFlow is able to reproduce the dependence of the shower shapes on the material properties at the few-percent level with larger differences only in the tails of the distributions.https://doi.org/10.1140/epjc/s10052-025-14604-0 |
| spellingShingle | Johannes Erdmann Jonas Kann Florian Mausolf Peter Wissmann Paraflow: fast calorimeter simulations parameterized in upstream material configurations European Physical Journal C: Particles and Fields |
| title | Paraflow: fast calorimeter simulations parameterized in upstream material configurations |
| title_full | Paraflow: fast calorimeter simulations parameterized in upstream material configurations |
| title_fullStr | Paraflow: fast calorimeter simulations parameterized in upstream material configurations |
| title_full_unstemmed | Paraflow: fast calorimeter simulations parameterized in upstream material configurations |
| title_short | Paraflow: fast calorimeter simulations parameterized in upstream material configurations |
| title_sort | paraflow fast calorimeter simulations parameterized in upstream material configurations |
| url | https://doi.org/10.1140/epjc/s10052-025-14604-0 |
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